Data Fusion for Satellite Remote Sensing
A fundamental problem in the analysis of satellite remote sensing data is that of “merging” two data sets which provide complementary information. Usually, the two data sets will have different observation grids: the pixel footprints are non-nested, of different resolutions, and are oriented differently. The most common ad-hoc solution is to either aggregate the data up to a coarse, common resolution, or to “match-up” pixel centers. In this project, we seek to develop a principled methodology for fusing (merging) the data into a statistically consistent data set suitable for both exploratory and inferential science analysis.